Assessing service quality for networks and network equipment can be tedious and time consuming. Installers, technicians, and care agents are often subject to guessing and frustrating trial-error evaluation of network impairments. This sometimes results in unusually high installation, troubleshooting, and resolution times due to unanticipated challenges in diagnosing network issues.
Current network service quality assessment relies on visual inspection of network signals (e.g., radio frequency waveforms, ripple count groupings, etc.). However, existing methods are not sufficient for analysis of data containing multiple distinct impairments due to the superimposition of impairments. Furthermore, frequency dependent waveform signatures generated from signal level or energy level measurement can be masked by echoes on the network. Visual inspection is often labor intensive due to the size and complexity of the problem, as is manually troubleshooting the network using conventional methods. In some instances, the overall service quality of a larger area can mask problems affecting smaller areas, making problem detection in smaller areas especially difficult. Accordingly, current methods do not provide a sufficient means for assessing service quality of a network.